skills.rest
rocklambros
rocklambrosCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

building-rag-eval-set

Build stratified RAG evaluation sets with source attribution and human review.

2|Updated May 23, 2026
Search Tags:#versioning#rag#evaluation#retrieval#dataset#human review#stratified splits
One-click install
npx skills add https://github.com/rocklambros/rcs --skill building-rag-eval-set
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: building-rag-eval-set
Source: https://github.com/rocklambros/rcs/tree/main/skills/ml-datasci/building-rag-eval-set
Command: npx skills add https://github.com/rocklambros/rcs --skill building-rag-eval-set

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill turns a raw document corpus into a defensible evaluation set for retrieval-augmented generation, so teams can stop relying on ad-hoc questions and guesswork.

Core Features & Use Cases

  • Greenfield evaluation design: Creates calibration, held-out, and adversarial splits instead of a single flat test set.
  • Failure attribution: Requires source document IDs and source spans on every non-absent-topic question so retrieval errors can be separated from generation errors.
  • Human-reviewed gold data: Forces review and ground-truth verification before any row enters the golden set, reducing noise and contamination.
  • Production discipline: Locks the set with versioning, dataset hashing, and a documented review protocol for repeatable downstream audits.
  • Use case: Build a proper RAG eval for internal HR policies, legal documents, medical corpora, or other custom domains where public benchmarks do not fit.

Quick Start

Ask the Skill to build a RAG evaluation set for your corpus and specify your reviewers, target size, and whether you need calibration, held-out, and adversarial splits.

Frequently Asked Questions about building-rag-eval-set

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a RAG evaluation set that separates retrieval failures from generation failures?▼

A rigorous RAG evaluation set requires source document IDs and source spans on every question to attribute retrieval errors separately from generation errors. This Skill enforces that attribution alongside human review to ensure defensible measurement.

What's the best way to structure a RAG evaluation dataset for custom-domain corpora?▼

Structuring a RAG evaluation dataset involves creating calibration, held-out, and adversarial splits instead of a single flat test set. This approach supports greenfield test construction for custom domains like HR policies or medical documents where public benchmarks do not fit.

Why do I need human review and dataset hashing for retrieval-augmented generation evaluation?▼

Human review and dataset hashing are needed to lock the RAG evaluation set with versioning and a documented protocol, ensuring ground-truth verification and repeatable downstream audits while reducing noise and data contamination.

Can I use stratified splits and adversarial stress cases for RAG benchmark replacement?▼

Yes, stratified splits and adversarial stress cases can be applied for RAG benchmark replacement decisions. This Skill constructs targeted calibration and held-out splits to measure performance against edge cases specific to your custom-domain corpus.

What are the limitations of using ad-hoc questions for RAG evaluation?▼

Ad-hoc questions lack source-document attribution, stratified splits, and versioned documentation, making it impossible to separate retrieval failures from generation failures or maintain repeatable downstream audits for production-grade RAG systems.

Related Skills

Explore More Data & Analytics Skills→
lucifertrjlucifertrj
17

nltk-nlp-text-cleaning

Clean, tokenize, and normalize English text with NLTK for NLP pipelines.

Community
Advanced
majidraza1228majidraza1228

validate-evaluator

Calibrate an LLM judge against human labels using train/dev/test splits.

Community
Advanced
voxel51voxel51
37

fiftyone-model-evaluation

Evaluate model predictions against ground truth using FiftyOne evaluation protocols.

Official
Intermediate
JhabbigJhabbig

llm-evaluation

Design evaluation frameworks for LLM applications with automated and human scoring.

Community
Advanced